Unveiling the role of network andsystems biology in drug discoveryAlbert Pujol1,2, Roberto Mosca1, Judith Farres2 and Patrick Aloy1,3

1 Institute for Research in Biomedicine and Barcelona Supercomputing Center. c/Baldiri i Reixac 10-12, 08028 Barcelona, Spain2 Anaxomics Biotech. c/Balmes 89, 08008 Barcelona, Spain3 Institucio Catalana de Recerca i Estudis Avancats (ICREA), Pg. Lluıs Companys 23, 08010 Barcelona, Spain

Review

Network and systems biology offer a novel way ofapproaching drug discovery by developing models thatconsider the global physiological environment of proteintargets, and the effects of modifying them, withoutlosing the key molecular details. Here we review somerecent advances in network and systems biology appliedto human health, and discuss how they can have a bigimpact on some of the most interesting areas of drugdiscovery. In particular, we claim that network biologywill play a central part in the development of novelpolypharmacology strategies to fight complex multifac-torial diseases, where efficacious therapies will need tocenter on altering entire pathways rather than singleproteins. We briefly present new developments in thetwo areas where we believe network and system biologystrategies are more likely to have an immediate contri-bution: predictive toxicology and drug repurposing.

IntroductionFifty years ago, the first steps in a drug discovery processwere mostly driven by the response to assayed moleculesobserved in animal models, what we would today call‘advanced pre-clinical tests’. The rating of potential drugcompounds was thus based on their ability to generate thedesired detectable changes in the physio-pathologicalstates of the animals, and little attention was paid to other,more biochemical, aspects such as binding affinities of thecompound to its primary targets or its specificity. Thismeans that most early ‘go’ or ‘no-go’ decisions on differentmolecules were taken on the basis of their global pharma-cological properties under physiological conditions [1].

In the early 1980s, the development and broad imple-mentation of methods to isolate and study individual cellsand molecules significantly increased our understanding ofthe individual players taking part in complex biologicalprocesses, placingmolecular biology in a privileged positionamong biological sciences. Recent years have seen theclimax of these component-based approaches with genomesequencing projects providing nearly complete lists of thegenes and gene products found in the human body [2], firstdrafts of connectivity maps between proteins [3–5], geneexpression profiles formanydifferent tissues and conditions[6,7], and initial quantifications ofmetabolites [8,9]. This bigsuccess experienced by molecular biology also triggered adeep change of strategy in the drug discovery process:knowing the molecules involved in a certain pathological

Corresponding author: Aloy, P. (patrick.aloy@irbbarcelona.org)

0165-6147/$ – see front matter � 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.tips.2009.

process permitted pharmaceutical companies to ‘handpick’those proteins that they thought would make a good target,and rationally design compounds to interfere with them. Asa consequence, the initial discovery phases removed thetargets from their physiological context to study them atquasi-atomic level, and focused on the optimization of thetarget-compound duet, placing a special emphasis inincreasing binding affinity and target selectivity [10]. Thus,the criteria to evaluate the potential of a novel moleculeshifted from a strict physiological observation of the resultsobtained with the assayed compounds to a molecular one,where the best lead chemicals were those displaying astrong bindingwith the target protein and a good specificityprofile (i.e. binding to only one target).However, inductionofa disease state is often the result of an incredibly complexcombination of molecular events [11] and, despite severalsuccess stories, the reductionist approach adopted also hadstriking consequences. For instance, many promising drugcandidates failed the last (and most expensive) clinicalphases because theactionmechanisms of the pathways theytarget are incompletely understood or due to an inappropri-ate choice of in-vitro cellular models that proved ineffectiveat predicting off-target effects [1,12]. Approaches using net-work and systems biology hold the promise to take proteintargets back to their physiological context, considering amuch broader systemic perspective of their environmentwithout losing the molecular details. If successful, theseinterrelateddisciplines could represent thenext step indrugdiscovery, fostering the conception of mechanism-baseddrug design.

Biological interaction networks have been in the scien-tific limelight for nearly a decade, but it has been in thelast five years that the concept of network biology, and itsvarious applications, has became commonplace in thecommunity [13]. Despite being incomplete and error-prone, the initial versions of human interactome net-works [3–5] are of sufficient quality to provide usefulinformation [14]. Indeed, several models and analyticalmeasurements borrowed from graph theory have beentried to decipher biological networks, in particular Baye-sian networks, which have given the most promisingresults (Box 1). Partly because of these analytical tech-niques, network biology is already making importantcontributions to biomedical research, and it is clear thatit will play a pivotal role in the future of drug discovery.For instance, interaction discovery experiments, com-bined with computational analyses, have decipheredthe first draft of the human B-lymphocyte interactome

11.006 Available online 1 February 2010 115

Box 1. Network models for molecular interactions

Networks (i.e. graphs of connected nodes) are used in systems

biology to represent the different types of relationships between

biological entities such as genes, proteins, chemical compounds, and

transcription factors. These biological components typically represent

network nodes, and they are connected through edges that illustrate

their inter-relationships, from physical or functional associations to

metabolic pathways and regulatory networks.

Different models have been applied to their analysis, for instance,

Power Graphs [68] and the Information Flow Model [69]. Bayesian

networks, however, are the most commonly used due to their

capacity for expressing causal relationships, and learning from

incomplete datasets while avoiding overfitting problems.

In Bayesian networks, network nodes represent random variables

(e.g. functional classification of a protein) and the edges conditional

probabilities between them. In systems biology, they have been applied

to gene expression analysis, cellular networks inference [70], pathway

modeling [71], prediction and assessment of the quality of protein–

protein interactions [72] and functional annotation of proteins [73].

Another commonly used model are Boolean networks, in which

every node can have two states (on/off) representing, respectively, an

active or inactive gene. The state of a node is affected by the other

nodes (genes) connected to it, allowing representation of complex

regulatory systems. Boolean networks, together with Bayesian

networks, have also been used to model the dynamical behavior of

gene regulatory circuits [74].

Apart from these networks, recent years have seen an ever-growing

tendency to combine different types of networks to attempt modeling

of more complex types of inference [15,75–77].

Computational measurements for the analysis of molecular interac-tion networks

There are several topological aspects of biological networks that have

been proved to be useful for inferring functional properties (see [78]

for a review). The most common aspects are listed below.

� Network statistics and topological features such as node centrality,

between-ness, degree distribution of nodes, clustering coefficient,

shortest path between nodes and robustness of the network to the

random removal of single nodes.

� Modularity refers to the identification of sub-networks of inter-

connected nodes that might represent molecules physically or

functionally linked that work coordinately to achieve a specific

function [79–81].

� Motif analysis is the identification of small network patterns (or

subgraphs) that are over-represented when compared with a

randomized version of the same network. Discrete biological

processes such as regulatory elements are often composed of such

motifs [82,83].

Furthermore, recently developed network alignment and com-

parison tools [84–86] can identify similarities between networks

(such as common subgraphs) and have been used to study

evolutionary relationships between protein networks of several

organisms [87].

Review Trends in Pharmacological Sciences Vol.31 No.3

from indirect expression data, which helped to identifyderegulated interactions in specific pathological orphysiological phenotypes, as well as causal lesions inseveral well-studied B-cell malignancies [15]. Network-biology approaches have also been successfully used intumor research, and the analysis of the disease-networkassociated to BRCA1 permitted identification of novelgenes associated with a higher risk of breast cancer, inthe process uncovering a genetic link with centrosomedysfunction [16]. Also, a strategy based on monitoringtranscriptional responses induced by the perturbationof candidate regulators have permitted derivation ofregulatory networks mediating pathogen responsesin primary mammalian cells [17]. The most excitingdiscovery, however, is that current models are alreadysufficiently accurate to allow global properties of thenetworks to emerge. A recent study illustrated how func-tional properties arising directly from the topology of thenetwork can be used to identify novel markers for breastcancer metastasis [18]. Additionally, by integrating co-expression and genotypic data, it has been possible todemonstrate that complex traits such as obesity areemergent properties of molecular networks that are

116

modulated by complex genetic loci and environmentalfactors [19]. We anticipate that as the coverage, qualityand variety of protein interaction data improve, thenumber of approaches exploiting emerging networkproperties will grow.

The definition of systems biology is somehow elusivebecause it means different things to different people, fromthose who think of it as the development of large-scaleexperiments with the aim to understand how the whole isgreater than the sum of its parts to those that consider it abranch of mathematical biology and metabolic modelling.Yet, it is clear that systems biology should include thequantitative component that is missing in network biology[20]. This means that, unlike network biology, we shouldnot only identify the physical or functional relationshipsbetween the different components shaping a biologicalsystem, but also measure their concentrations in the stu-died physio-pathological cellular states and the kineticparameters governing these interactions. Fortunately,recent years have seen the development of many tech-niques that can provide quantitative measurements inhigh-throughput experiments devoted to unveil the cellnetworks underlying biological principles (Box 2). Taken

Box 2. Quantitative techniques for systems biology

Several quantitative experimental techniques have been developed

in recent years that are applied in approaches using network biology

and systems biology to human health.

Expression-quantitative trait locus (e-QTL) is the composition of

classical QTL analyses with gene expression profiling (i.e. by DNA

microarrays). It provides information about the expression variation

of genetically diverse individuals, and in recent years has been used

to identify networks of genes involved in disease pathogenesis [88].

Quantitative mass spectrometry is an analytical technique that

permits the identification of the chemical composition of com-

pounds on the basis of the mass-to-charge ratios of charged

particles. Technical advances in mass spectrometry-based proteo-

mics has allowed it to be applied to measure changes in protein

abundance, post-translational modifications and protein–protein

interactions in mutants at the proteome scale [89,90].

Quantitative surface plasmon resonance (SPR) is an optical tech-

nique based on a biosensor that measures molecular binding events

at a metal surface by detecting changes in the local refractive index.

Coupling of SPR with new protein array technologies may allow

development of high-throughput SPR methods to analyze pathways,

screen drug candidates, and monitor protein–protein interactions

[91,92].

Isothermal titration calorimetry (ITC) is a quantitative technique that

can directly measure the binding affinity (Ka), enthalpy changes (DH),

and binding stoichiometry (n) of the interaction between two or more

molecules in solution by measuring the heat released or absorbed

during a biomolecular binding event.

Fluorescence techniques benefit from the labeling of bio-molecules

with fluorophores to identify thevariation of their concentration in time

during a binding event. Fluorescence detection by confocal micro-

scopy and fluorescence spectroscopy techniques have been success-

fullyapplied to determinationof dynamicconstants forprotein binding

[93,94].

Nuclear magnetic resonance (NMR) spectroscopy uses the shift on

the resonant frequencies of the nuclei present in a sample to obtain

physical, chemical, electronic and structural information about mol-

ecules. It has been shown to be applicable to the quantitative charac-

terization of dynamic binding constants for protein–DNA [95] and

protein–protein complexes [96].

Review Trends in Pharmacological Sciences Vol.31 No.3

together, the data generated by these novel techniquesshouldmove forward systems biology to allow simulation ofhow the molecules function in coordination to achieve aparticular outcome and, consequently, confer it the tre-mendous power of predicting the result of yet unstudiedperturbations (i.e. the effect of modulating the function of agiven protein). Despite the lack of quantitative data,models based on systems biology already account for afew success stories in the pharmaceutical and biotechno-logical sectors. For instance, inclusion of synthetic path-ways designed from a systemic perspective in yeast haveled to a drastic reduction of the production costs of arte-misinin, a compound that has proved to be very effectiveagainst multidrug-resistant strains of themalaria parasitePlasmodium falciparum [21]. Unfortunately, the detailedlevel of knowledge required to apply systems-biology strat-egies to their full potential is available for only very smalland controlled biological systems such as some bacteria[22] or intracellular organelles [23], which virtually pre-cludes direct application of these models to human health.In the absence of any ‘blockbuster’ drug developed with aparamount contribution of systems-biologymodels, it is notsurprising that the pharmaceutical industry remains scep-tical about these novel technologies [24]. In the followingsections, we will discuss how network and systems biology

can have a big impact in some of the most important areasof drug discovery.

Complex diseases require complex therapeuticapproachesMathematical systems theory states that ‘the scale andcomplexity of the solution should match the scale andcomplexity of the problem’ [25], and biology is no exception.In recent years, it has become apparent thatmany commondiseases such as cancer, cardiovascular disease as well asmental disorders are much more complex than initiallyanticipated because they are often caused by multiplemolecular abnormalities, rather than being the result ofa single defect [26,27]. Recent outstanding works in cancergenetics have shown that there are many diverse geneticroutes that might perturb certain cellular pathways, lead-ing to the origination of, for instance, pancreatic cancer[28–30]. Moreover, these studies highlighted that, despitethe great genetic variation observed among each type ofcancer in different patients, they clearly share commonfeatures at the protein pathway level, which supports theview that cancer is a ‘disease of pathways’. It thereforeseems clear that modulating a single target, even with avery efficient drug, is unlikely to yield the desired outcome.A growing perception is that we should increase the level ofcomplexity of our proposed therapies by changing the waywe think about complex diseases from a gene-centric to anetwork-centric view [31].

This novel network perspective of complex diseaseshas many implications in the drug discovery processbecause the entity that needs to be targeted and modu-lated must change from single proteins to entire molecu-lar pathways and cell networks. A detailed interactionmap of the studied disease becomes essential to rationallydecide which are the optimal points for therapeuticintervention [32] (i.e. the collection of drug targets tomodify ill-functioning cellular routes). Using reliableinteractome maps to select these strategic network nodesenables consideration of the robustness of the system,which is not possible in gene-centered approachesbecause they mostly disregard the biological context ofthe target. Biological systems such as disease states are,in general, resistant to perturbations and they can main-tain their functions through different mechanisms suchas back-up circuits and fail-safe mechanisms (i.e. redun-dancy and diversity) [33]. Therefore, the selection processof putative drug targets should also consider their posi-tioning in the network, preferring those enclaves that areessential to drive the network traffic and, simultaneouslycan avoid back-up circuits that could neutralize the drugeffect. Interestingly, recent observations have also shownthat the wiring of interaction networks can change fromhealthy to diseased states (see [34] for a recent review),and charting such changes would be an excellent guide tosuggest potential points of intervention. For instance,several signaling pathways involved in liver functionshow different functional wiring in receptor–nucleusdownstream routes when comparing normal hepatocyteswith HepG2-transformed cells, an observation that hasalready caught the attention of the pharmacueticalindustry [35].

117

Review Trends in Pharmacological Sciences Vol.31 No.3

Network-centric therapeutic approaches imply to targetentire pathways rather than single proteins. The final goalof these approaches would not only be to identify drugs thatcan be prescribed together, but a combination of targets andmodulators acting on different therapeutic areas that canproduce more-than-additive (e.g. synergistic) effects trig-gered by actions converging at a specific pathway site. Thismight sound like an impossible quest, but>100 drug syner-gistic cases have been recently reported or are currentlybeing commercialized (see [36] for a review). Experimentaltherapies against breast cancer metastasis represent one ofthe few examples where drug combinations have beenrationally designed from the extensive knowledge of thepathways involved. In this case, after elucidating that theepidermal growth factor receptor (EGF-R) ligand eregulin(EREG), the cyclo-oxygenase COX2 and the matrix metal-loproteases 1 and 2 (MMP1, MMP2) were key genes totrigger cancer metastasis from the breast to the lungs, cellswere treated with the anti-EGFR antibody cetuximab, theCOX2 inhibitor celecoxib, and the broad-spectrum MMPinhibitor GM6001, to produce a spectacular reduction ofmetastatic extravasation [37].

The synergistic drug effects obtained through combi-nation of two or more compounds can be the result of verydiverse strategies. This can be from direct interferencewith several points in the same pathway to negativeregulation of network compensatory responses (i.e. back-up circuits), drug resistance sources, or anti-target andcounter-target activities. An example of a synergistic effectreached through the complementary action on two differ-ent targets of cross-talking pathways is the combination oftaxane and gefinitib in anti-cancer therapies (Figure 1).Taxane produces an anti-cancer effect by inducing apop-tosis and disruption of microtubule dynamics. However,the observed cross-talk between the EGF-R and hypoxia-inducible factor-1 alpha (HIF1a) pathways increases theresistance to apoptosis by upregulating survival. In thiscase, the addition of gefinitib, with its EGF-R tyrosinekinase inhibiton activity, offsets the counteractive EGF-R–hypoxia cross-talk in resisting the pro-apoptosis activityof taxane displaying a strong synergistic effect in breastcancer MCF7/ADR cells [38].

Drug combinations can also display pharmaco-kineti-cally potentiative or reductive effects such that the thera-peutic activity of one drug is enhanced or reduced byanother drug via regulation of its absorption, distribution,metabolism and excretion (i.e. ADME). A good example ofthis is the widely used combination of amoxicillin andclavulanate to treat bacterial infections (particularly inchildren). Amoxicilin inhibits synthesis in the cell walls ofbacteria. Clavulanate is an inhibitor of b-lactamase, theenzyme responsible for amoxicillin destruction. Whenadministered together, these two drugs produce verypotent antibacterial activity because clavulanate main-tains the levels of amoxicillin in the cell wall by inhibitingits degradation [39]. Finally, a very interesting case of drugcombinations are coalistic combinations, in which all of thedrugs are individually inactive, but become active in com-bination [40].

Using drug cocktails to target multiple enclaves toproduce synergistic effects against a certain disease sounds

118

like a promising idea and, as described above, has achievedseveral remarkable results. However, there is the percep-tion among drug development companies that simul-taneous administration of several compounds wouldnecessarily imply more undesired off-target effects. For-tunately, this is not necessarily the case. Use of severaldrugs with synergistic effects acting on unrelated targetsmight permit considerable reduction of the dose of eachindividual compound and thus the derived adverse events[41]. Conversely, it has recently been shown that synergis-tic drug combinations tend to display a greater selectivity,being more specific to particular cellular phenotypes thansingle drugs, thus synergistic toxicity is not expected [42].We anticipate that the considerable efforts currently beingdevoted to chart the cell networks related to complexdiseases, in combination with systems-biology method-ologies to identify and validate target combinations andoptimize multiple structure–activity relationships whilemaintaining drug-like properties, will permit rapid devel-opment of network-based therapies [43].

The more the merrierFor almost a century drug discovery was driven by thequest for ‘magic bullets’ that acted by targeting oneparticular and critical point or step in a disease process,and thus effect cure with few other consequences. Manydrugs have been designed rationally in this manner but,after 20 years, it appears that this single target-basedapproach does not guarantee success and might not bethe best strategy. As well as the impossibility of finding asingle intervention point to efficaciously fight complexdiseases, some specific drugswere directed towards targetsthat resulted not essential for the pathophysiology of thedisease, eventually became inactive because of graduallyincreasing resistance of cells [44] or generated unpredictedeffects on off-target biochemical mechanisms [12] .

Even one of the most recent success stories of rationaldrug design of a magic bullet appeared to be not as suc-cessful as initially thought. The Novartis blockbuster drugimatinib mesylate (gleevec) was designed to act on a singleaberrant protein expressed in cancerous cells, specificallykilling them while leaving healthy cells unharmed. How-ever, it was soon discovered that it also bound with sig-nificant affinities to the platelet-derived growth factorreceptor (PDGF-R) and c-kit, which confers it with remark-able properties against gastrointestinal stromal tumors,directly linked to malfunctioning of the latter protein [45].Today, the emerging picture is that drugs rarely bindspecifically to a single target, challenging the concept ofmagic bullets, which is not necessarily prejudicial.

Indeed, recent analysis of drug and drug-target net-works show a rich pattern of interactions among drugsand their targets in which drugs acting on single targetsappear to be the exception [46,47]. Likewise,many proteinsare targeted by more than one drug containing distinctchemical structures [48,49]. Consequently, a concept thatis increasingly gaining acceptance as a way to treat poly-genic diseases, both from target and drug perspectives, is‘polypharmacology’ [43]. As discussed above, the analysisof the biological networks associated with a given diseasecan suggest multiple targets to achieve the desired

Figure 1. Synergistic anti-cancer effect produced by the combination of taxane and gefinitib.

Taxane is an anti-cancer drug that works by disrupting microtubules through its binding to b-tubulin. It also induces expression of the tumor suppressor gene p53 and CDK

inhibitor p21, and downregulates bcl-2, eventually leading to apoptosis. However, it also induces apoptosis resistance through stimulation of the EGF-R pathway.

Phosphorylated EGF-R activates PI3K, which subsequently activates AKT1. The latter has a positive role in the activation of the complex between the HIF1-a and HIF1-b

(ARNT) transcription factors, forming a complex that binds to a promoter region called HRE which triggers the transcription of survival, a strong inductor of apoptosis

resistance by inhibiting caspase activity. Gefinitib, an EGF-R tyrosine kinase inhibitor, can re-establish the anti-cancer effect of taxane and also has pro-apoptotic activity by

inducing the expression of the CDK inhibitors p27 and p21, and decreasing the enzyme activity of MMP2 and MMP9, resulting in a potent synergistic effect in breast cancer

MCF7/ADR cells.

Review Trends in Pharmacological Sciences Vol.31 No.3

outcome. Conversely, we can also envisage new moleculesspecifically synthesized from building blocks that enablethem to bind to multiple targets, although probably withlower affinities (i.e. ‘magic shotguns’ or ‘dirty drugs’). Thispolypharmacology strategy has been successfully appliedto some central nervous system (CNS) disorders [50],Alzheimer’s disease [51], oncogenic mutations [52], andlooks very promising for identification of effective antibac-terial drugs [53]. Similarly, multi-target antibodies (in theform of diabodies, triabodies, tetrabodies and recombinantpolyclonal antibodies) are also increasingly used in cancertherapy to delay the development of resistance [54]. Theefficacy of such therapies can be explained by the fact thatdrugs targeting different proteins in the disease network or

pathway could trigger a synergistic response, and thattheir combination can eliminate compensatory reactions,thereby avoiding a drug-resistance denouement [55].

Although the single-target approach remains the mainstrategy in big pharmaceutical companies, some remark-able efforts are being put into the development and mar-keting of ‘promiscuous’ drugs that can block, for instance,several kinases simultaneously [56]. However, one of theprincipal limitations of the rational design of dirty drugs isthe difficulty in designing reliable assays to screen for thebest compounds that can hitmultiple targets [57]. A deeperunderstanding of the cellular processes and molecularnetworks related to the disease at which the drug is aimedwould help in the selection process because it would permit

119

Review Trends in Pharmacological Sciences Vol.31 No.3

the identification of all the targets that need to be influ-enced, either positively or negatively, and the design of thepertinent cellular assays.

The (not so) low-hanging fruitsIt is expected that themore contextualized view provided bynetwork and systems biology will, in the long-term, revolu-tionize current drug discovery strategies, propelling themfromtheclassicalempiricismtoamechanism-basedrationaldesign of global therapies [58]. However, there are twoimportant areas in drug discovery in which network-basedapproaches are likely to make key contributions in the nearfuture: predictive toxicology and drug repurposing.

Accurate prediction of potential adverse reactions tocompounds in the early stages of drug development pipe-lines is one of the major challenges in the pharmaceuticalindustry. Today, evaluation of the safety and toxicology ofdrugs is largely empirical, centered in the chemical proper-ties of the compounds and very prone to errors. Integratingnetwork biology and network chemistry to identifyputative secondary targets for a given compound or explorepotential downstream effects of blocking the action of a keynode in the biological network could rapidly provide analternative to the way drug candidates are assessed. Forinstance, Pfizer has developed an in-vitro testing strategybased on Boolean models of hepatocyte death–survivalpathways [59] and cell imaging to predict drug-induced

Figure 2. Network biology applied to predictive toxicology and drug repurposing.

The disease-associated networks for diabetes (dark-blue dashed lines) and nausea (ligh

cause some frequent adverse effects if their normal functioning is affected (red nodes). I

nodes). Intense research is being carried out to create models that can identify the area

related to network connectivity. If successful, these models could help to identify poten

the discovery process, and to rationally design the toxicity tests needed to check the

addition, detailed description of the molecular networks associated with certain disea

indication in key enclaves of the network describing a different disease, thereby sugge

120

liver injury that has accuracies approaching 60% and lowrates of false-positives results [35]. Elsewhere, it is nowpossible to predict chemical toxicities with increasingaccuracy by considering the physical or functional proxi-mity in a network of the target for a given compound andproteins known to cause some undesired side-effects iftheir function is affected [60] (Figure 2). Despite theseadvances, most of these toxicology models are qualitativeand, to be most valuable, they should incorporate thequantitative components necessary to allow appropriateprediction of, for instance, dose-related effects.

Partially due to limited success in novel pipeline pro-ducts, one area of research that is becoming increasinglyimportant in drug development is drug repurposing, i.e.finding new therapeutic uses for approved drugs. Themainadvantage of this approach is that, theoretically, it shoulddrastically reduce the risks of drug development becausethe starting point is usually approved compounds withwell-established safety and bioavailability profiles, provenformulation and manufacturing routes, and well-charac-terized pharmacology. In principle, all of these factorsshould facilitate repurposed compounds to enter clinicalphases more rapidly and at a lower cost than novel chemi-cal entities [61].

The rationale behind drug repurposing is based on twoconcepts discussed in previous sections. First, we haveseen how it is common for a drug to interact with multiple

t-blue dashed lines) contain several proteins that have been reported to be likely to

n addition, the networks contain drug targets annotated to a specific disease (green

s of influence of proteins leading to undesired effects, and to explore how they are

tial drug targets that are likely to trigger severe adverse reactions at early stages of

safety of other drug targets under the area of influence of a certain red node. In

ses can highlight the existence of validated drug targets for a given therapeutic

sting candidates for drug repurposing (i.e. finding new indications for a target).

Review Trends in Pharmacological Sciences Vol.31 No.3

protein targets (i.e. dirty drugs). This has triggered onerepurposing approach based on the identification of sec-ondary targets for a given drug in a different therapeuticarea. One of the first examples of this approximation to findnew targets for a marketed compound was the repurposingof thalidomine in the 1990s. Thalidomine was initiallyprescribed to treat nausea and insomnia in pregnantwomen, and had to be withdrawn because it was foundto cause severe defects in the fetus. It was subsequentlyobserved that thalidomine also had very pronounced anti-angiogenic and immunomodulatory effects on the tumorgrowth factor-alpha (TGF-a) pathway, andwas reapprovedfor the treatment of leprosy [62]. The second scientificconcept that supports drug repurposing is the high con-nectivity among apparently unrelated cellular processes.This could mean that a given target might be relevant toseveral diseases (Figure 2). A classical example is that offinasteride, a compound that targets the 5a-reductase,which was originally approved for the treatment of pros-tate enlargement. The target enzyme that converts testos-terone to dihydrotestosterone was found to have a role inhair loss in males, so finastride was also approved for theprevention of baldness [63].

The concept of finding new indications for approveddrugs can also be applied in a semi-blind approach thatscreens existing compounds against a multitude of targets,and identifies possible therapeutic benefits or side-effectsin an unbiased manner [64]. For instance, by testingthousands of combinations of off-patent drugs in cell-basedassays, it has been found that two drugs with differentindications (one antipsycothic and one antiprotozoal) exhi-bit unexpected antitumoral activity [65]. Unfortunately,the design of the screens ismainly based on the availabilityof in-house cell assays, and we foresee that the inclusion ofnetwork information and systems-biology models couldrapidly improve the repurposing outcome. For instance,identification of protein targets for a given drug thatoccupy a relevant position in a cell network associatedwith a certain disease could give valuable informationregarding a new therapeutic area in which its activitycould be tested.

Concluding remarksThe reductionism that has prevailed in drug discovery inthe last 20 years has resulted in many promising drugcandidates failing in the final phases of clinical testing.This has been mainly due to a lack of knowledge aboutdisease pathways at the molecular level, which often led tounforeseen adverse effects and unacceptable toxicity pro-files. Network biology and systems biology offer a novelway of approaching drug discovery through the develop-ment of models that can place protein targets in theirphysiological context, offering a global perspective of theirenvironment without losing the key molecular details. Thehigh interconnectivity observed in disease-associated net-works suggests that, to be most effective, new therapeuticstrategies to fight complex diseases should focus on target-ing entire cellular pathways rather than single proteins,either through the use of drug cocktails or promiscuouscompounds. However, and despite the growing evidence,multi-target approaches are still anecdotal in the discovery

pipelines of big pharmaceutical companies. This can bepartially attributed to the lack of robust network modelsable to optimally balance multiple drug activities on differ-ent targets with the control of undesired off-target effects.We anticipate that considerable international efforts, suchas the ongoing initiatives to chart disease-related inter-action maps [66] and the phenotypic effects of targetingmultiple proteins inmodel organismswith chemical probes[67], will soon permit refinement of systems-biologymodelsto the point where they can be routinely applied to some ofthe most important areas of drug development.

AcknowledgementsWe thank Teresa Sardon (CRG) for critically reading the manuscript. PAacknowledges the financial support received from the Spanish Ministeriode Educacion y Ciencia through the grant BIO2007-62426 and theEuropean Commission under FP7 Grant Agreement 223101(AntiPathoGN).

References1 Butcher, E.C. (2005) Can cell systems biology rescue drug discovery?

Nat. Rev. Drug Discov. 4, 461–4672 Lander, E.S. et al. (2001) Initial sequencing and analysis of the human

genome. Nature 409, 860–9213 Rual, J.F. et al. (2005) Towards a proteome-scale map of the human

protein-protein interaction network. Nature 437, 1173–11784 Stelzl, U. et al. (2005) A human protein-protein interaction network: a

resource for annotating the proteome. Cell 122, 957–9685 Ewing, R.M. et al. (2007) Large-scale mapping of human protein-

protein interactions by mass spectrometry. Mol. Syst. Biol. 3, 896 Barrett, T. et al. (2007) NCBI GEO: mining tens of millions of

expression profiles—database and tools update. Nucleic Acids Res.35, D760–765

7 Parkinson, H. et al. (2007) ArrayExpress—a public database ofmicroarray experiments and gene expression profiles. Nucleic AcidsRes 35, D747–750

8 Pearson, H. (2007) Meet the human metabolome. Nature 446, 89 Wishart, D.S. et al. (2007) HMDB: the Human Metabolome Database.

Nucleic Acids Res. 35, D521–52610 Drews, J. (2006) Case histories, magic bullets and the state of drug

discovery. Nat. Rev. Drug Discov. 5, 635–64011 Lindsay, M.A. (2005) Finding new drug targets in the 21st century.

Drug Discov. Today 10, 1683–168712 van der Greef, J. andMcBurney, R.N. (2005) Innovation: Rescuing drug

discovery: in vivo systems pathology and systems pharmacology. Nat.Rev. Drug Discov. 4, 961–967

13 Russell, R.B. and Aloy, P. (2008) Targeting and tinkering withinteraction networks. Nat. Chem. Biol. 4, 666–673

14 Venkatesan, K. et al. (2009) An empirical framework for binaryinteractome mapping. Nat. Methods 6, 83–90

15 Mani, K.M. et al. (2008) A systems biology approach to prediction ofoncogenes and molecular perturbation targets in B-cell lymphomas.Mol. Syst. Biol. 4, 169

16 Pujana, M.A. et al. (2007) Network modeling links breast cancersusceptibility and centrosome dysfunction. Nat. Genet. 39, 1338–

134917 Amit, I. et al. (2009) Unbiased reconstruction of a mammalian

transcriptional network mediating pathogen responses. Science 326,257–263

18 Chuang,H.Y. et al. (2007) Network-based classification of breast cancermetastasis. Mol. Syst. Biol. 3, 140

19 Chen, Y. et al. (2008) Variations in DNA elucidate molecular networksthat cause disease. Nature 452, 429–435

20 Kitano, H. (2005) International alliances for quantitative modeling insystems biology. Mol. Syst. Biol. 1, 0007

21 Ro, D.K. et al. (2006) Production of the antimalarial drug precursorartemisinic acid in engineered yeast. Nature 440, 940–943

22 Bonneau, R. et al. (2007) A predictive model for transcriptional controlof physiology in a free living cell. Cell 131, 1354–1365

23 Stuart, L.M. et al. (2007) A systems biology analysis of the Drosophilaphagosome. Nature 445, 95–101

121

Review Trends in Pharmacological Sciences Vol.31 No.3

24 Abbott, A. (2008) Pharmaceutical futures: a fiendish puzzle. Nature455, 1164–1167

25 Bar-Yan, Y. (2004)Making Things Work: Solving Complex Problems ina Complex World, NECSI Knowledge Press

26 Scriver, C.R. and Waters, P.J. (1999) Monogenic traits are not simple:lessons from phenylketonuria. Trends Genet. 15, 267–272

27 Dipple, K.M. and McCabe, E.R. (2000) Modifier genes convert ‘‘simple’’Mendelian disorders to complex traits. Mol. Genet. Metab. 71, 43–50

28 Jones, S. et al. (2008) Core signaling pathways in human pancreaticcancers revealed by global genomic analyses. Science 321, 1801–1806

29 Parsons, D.W. et al. (2008) An integrated genomic analysis of humanglioblastoma multiforme. Science 321, 1807–1812

30 Cancer Genome Atlas Research Network (2008) Comprehensivegenomic characterization defines human glioblastoma genes andcore pathways. Nature 455, 1061–1068

31 Jones, D. (2008) Pathways to cancer therapy.Nat. Rev. Drug Discov. 7,875–876

32 Pache, R.A. et al. (2008) Towards a molecular characterisation ofpathological pathways. FEBS Lett. 582, 1259–1265

33 Kitano, H. (2004) Biological robustness. Nat. Rev. Genet 5, 826–83734 Zanzoni, A. et al. (2009) A network medicine approach to human

disease. FEBS Lett. 583, 1759–176535 Xu, J.J. et al. (2008) Cellular imaging predictions of clinical drug-

induced liver injury. Toxicol. Sci. 105, 97–10536 Jia, J. et al. (2009) Mechanisms of drug combinations: interaction and

network perspectives. Nat. Rev. Drug Discov. 8, 111–12837 Gupta, G.P. et al. (2007) Mediators of vascular remodelling co-opted for

sequential steps in lung metastasis. Nature 446, 765–77038 Takabatake, D. et al. (2007) Tumor inhibitory effect of gefitinib

(ZD1839, Iressa) and taxane combination therapy in EGFR-overexpressing breast cancer cell lines (MCF7/ADR, MDA-MB-231).Int. J. Cancer 120, 181–188

39 Matsuura, M. et al. (1980) Combined antibacterial activity ofamoxicillin with clavulanic acid against ampicillin-resistant strains.Antimicrob. Agents Chemother. 17, 908–911

40 Johnson, M.D. et al. (2004) Combination antifungal therapy.Antimicrob. Agents Chemother. 48, 693–715

41 Zimmermann, G.R. et al. (2007) Multi-target therapeutics: when thewhole is greater than the sum of the parts. Drug Discov. Today 12, 34–

4242 Lehar, J. et al. (2009) Synergistic drug combinations tend to improve

therapeutically relevant selectivity. Nat. Biotechnol. 27, 659–66643 Hopkins, A.L. (2008) Network pharmacology: the next paradigm in

drug discovery. Nat. Chem. Biol. 4, 682–69044 Schrattenholz, A. and Soskic, V. (2008) What does systems biology

mean for drug development? Curr. Med. Chem. 15, 1520–152845 Giles, F.J. et al. (2009) Class effects of tyrosine kinase inhibitors in the

treatment of chronic myeloid leukemia. Leukemia46 Yildirim, M.A. et al. (2007) Drug-target network. Nat. Biotechnol. 25,

1119–112647 Paolini, G.V. et al. (2006) Global mapping of pharmacological space.

Nat. Biotechnol. 24, 805–81548 Gregori-Puigjane, E. andMestres, J. (2008) A ligand-based approach to

mining the chemogenomic space of drugs. Comb. Chem. HighThroughput Screen. 11, 669–676

49 Nacher, J.C. and Schwartz, J-M. (2008) A global view of drug-therapyinteractions. BMC Pharmacol. 8, 5

50 Roth, B.L. et al. (2004)Magic shotguns versusmagic bullets: selectivelynon-selective drugs for mood disorders and schizophrenia. Nat. Rev.Drug Discov. 3, 353–359

51 Bolognesi, M.L. et al. (2009) Memoquin: a multi-target-directed ligandas an innovative therapeutic opportunity for Alzheimer’s disease.Neurotherapeutics 6, 152–162

52 Apsel, B. et al. (2008) Targeted polypharmacology: discovery of dualinhibitors of tyrosine and phosphoinositide kinases.Nat. Chem. Biol. 4,691–699

53 Rasko, D.A. et al. (2008) Targeting QseC signaling and virulence forantibiotic development. Science 321, 1078–1080

54 Todorovska, A. et al. (2001) Design and application of diabodies,triabodies and tetrabodies for cancer targeting. J. Immunol.Methods 248, 47–66

55 Kitano, H. (2007) A robustness-based approach to systems-orienteddrug design. Nat. Rev. Drug Discov. 6, 202–210

122

56 Choong, N.W. et al. (2009) Phase II study of sunitinib malate in headand neck squamous cell carcinoma. Invest. NewDrugs Aug, 4. Ahead ofprint

57 Peterson, J.J. and Novick, S.J. (2007) Nonlinear blending: a usefulgeneral concept for the assessment of combination drug synergy. J.Recept. Signal Transduct. Res. 27, 125–146

58 Henney, A. and Superti-Furga, G. (2008) A network solution. Nature455, 730–731

59 Saez-Rodriguez, J. et al. (2008) Flexible informatics for linkingexperimental data to mathematical models via DataRail.Bioinformatics 24, 840–847

60 Apic, G. et al. (2005) Illuminating drug discovery with biologicalpathways. FEBS Lett. 579, 1872–1877

61 Boguski, M.S. et al. (2009) Drug discovery. Repurposing with adifference. Science 324, 1394–1395

62 Cleach, L.L. et al. (1998) Reactions and interactions of some commonlyused systemic drugs in dermatology. Dermatol. Clin. 16, 421–429

63 Ekman, P. (1999) Finasteride in the treatment of benign prostatichypertrophy: an update. New indications for finasteride therapy.Scand. J. Urol. Nephrol. Suppl. 203, 15–20

64 O’Connor, K.A. and Roth, B.L. (2005) Finding new tricks for old drugs:an efficient route for public-sector drug discovery. Nat. Rev. DrugDiscov. 4, 1005–1014

65 Lee, M.S. et al. (2007) The novel combination of chlorpromazine andpentamidine exerts synergistic antiproliferative effects through dualmitotic action. Cancer Res. 67, 11359–11367

66 Charbonnier, S. et al. (2008) The social network of a cell: recentadvances in interactome mapping. Biotechnol. Annu. Rev. 14, 1–28

67 Lehar, J. et al. (2008) Combination chemical genetics.Nat. Chem. Biol.4, 674–681

68 Royer, L. et al. (2008) Unraveling protein networks with power graphanalysis. PLoS Comput. Biol. 4, e1000108

69 Missiuro, P.V. et al. (2009) Information flow analysis of interactomenetworks. PLoS Comput. Biol. 5, e1000350

70 Friedman, N. (2004) Inferring cellular networks using probabilisticgraphical models. Science 303, 799–805

71 Sachs, K. et al. (2005) Causal protein-signaling networks derived frommultiparameter single-cell data. Science 308, 523–529

72 Jansen, R. et al. (2003) A Bayesian networks approach for predictingprotein-protein interactions from genomic data. Science 302,449–453

73 Llewellyn, R. and Eisenberg, D.S. (2008) Annotating proteins withgeneralized functional linkages. Proc. Natl. Acad. Sci. U S A 105,17700–17705

74 Lahdesmaki, H. et al. (2006) Relationships between probabilisticBoolean networks and dynamic Bayesian networks as models ofgene regulatory networks. Signal Processing 86, 814–834

75 Kelley, R. and Ideker, T. (2005) Systematic interpretation of geneticinteractions using protein networks. Nat. Biotechnol. 23, 561–566

76 Zhu, J. et al. (2008) Integrating large-scale functional genomic data todissect the complexity of yeast regulatory networks. Nat. Genet. 40,854–861

77 Klamt, S. et al. (2009) Hypergraphs and cellular networks. PLoSComput. Biol. 5, e1000385

78 Barabasi, A.L. and Oltvai, Z.N. (2004) Network biology: understandingthe cell’s functional organization. Nat. Rev. Genet. 5, 101–113

79 Hartwell, L.H. et al. (1999) From molecular to modular cell biology.Nature 402, C47–52

80 Ravasz, E. et al. (2002) Hierarchical organization of modularity inmetabolic networks. Science 297, 1551–1555

81 Gavin, A.C. et al. (2006) Proteome survey reveals modularity of theyeast cell machinery. Nature 440, 631–636

82 Shen-Orr, S.S. et al. (2002) Network motifs in the transcriptionalregulation network of Escherichia coli. Nat. Genet. 31, 64–68

83 Milo, R. et al. (2002) Network motifs: simple building blocks of complexnetworks. Science 298, 824–827

84 Kelley, B.P. et al. (2004) PathBLAST: a tool for alignment of proteininteraction networks. Nucleic Acids Res. 32, W83–88

85 Kalaev, M. et al. (2008) NetworkBLAST: comparative analysis ofprotein networks. Bioinformatics 24, 594–596

86 Singh, R. et al. (2008) Global alignment of multiple protein interactionnetworks with application to functional orthology detection. Proc. Natl.Acad. Sci. U S A 105, 12763–12768

Review Trends in Pharmacological Sciences Vol.31 No.3

87 Suthram, S. et al. (2005) The Plasmodium protein network divergesfrom those of other eukaryotes. Nature 438, 108–112

88 Cookson, W. et al. (2009) Mapping complex disease traits with globalgene expression. Nat. Rev. Genet. 10, 184–194

89 Kumar, C. and Mann, M. (2009) Bioinformatics analysis of massspectrometry-based proteomics data sets. FEBS Lett. 583, 1703–1712

90 Gstaiger, M. and Aebersold, R. (2009) Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nat. Rev.Genet. 10, 617–627

91 Boozer, C. et al. (2006) Looking towards label-free biomolecularinteraction analysis in a high-throughput format: a review of newsurface plasmon resonance technologies. Curr. Opin. Biotechnol. 17,400–405

92 Nedelkov, D. (2007) Development of surface plasmon resonance massspectrometry array platform. Anal. Chem. 79, 5987–5990

93 Sudhaharan, T. et al. (2009) Determination of in vivo dissociationconstant, KD, of Cdc42-effector complexes in live mammalian cellsusing single wavelength fluorescence cross-correlation spectroscopy. J.Biol. Chem. 284, 13602–13609

94 Simonetta, M. et al. (2009) The influence of catalysis on mad2activation dynamics. PLoS Biol. 7, e10

95 Iwahara, J. et al. (2006) NMR structural and kinetic characterization ofa homeodomain diffusing and hopping on nonspecific DNA. Proc. Natl.Acad. Sci. U S A 103, 15062–15067

96 Sprangers, R. and Kay, L.E. (2007) Quantitative dynamics and bindingstudies of the 20S proteasome by NMR. Nature 445, 618–622

123